Enhancing Prediction Accuracy Model Performance. The Role of Directed Partial Correlation as a causal Filter for Time Series

Traditional time series forecasting models, especially in complex fields like finance, often struggle with two key problems: (1) false correlations that seem meaningful but lack real causation, and (2) tangled relationships between variables that standard methods cannot fully unravel. As a result, models may appear statistically sound but perform poorly in practice.
This research explores Causal Filtering—particularly Directed Partial Correlation (DPC)—as a preprocessing step to overcome these issues. Unlike conventional correlation-based approaches, DPC helps distinguish true causal links from misleading statistical patterns. To test its effectiveness, we compared DPC-enhanced regression against traditional methods using controlled simulated data. Predictive accuracy was measured using Adjusted R-squared, which accounts for model complexity.
Our findings show that DPC significantly improves both prediction accuracy and model stability by selecting fewer but more causally relevant variables. Hierarchical regression analysis confirmed that DPC-identified predictors align closely with the data’s true causal structure, unlike correlation-driven methods that often include irrelevant variables.
These results have important implications for time series forecasting. By focusing on real causal relationships rather than superficial correlations, DPC provides more reliable and interpretable models. This is especially valuable in fields like finance, where understanding true drivers—not just statistical patterns—is critical for decision-making. In summary, DPC offers a scientifically grounded way to enhance predictive modeling, making it both more accurate and more trustworthy for real-world applications.